National and Subnational estimates for Italy

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Italy. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively (see Methods for further explanation).

Table of Contents


Using data available up to the: 2020-04-03

Expected daily cases by region


Figure 1: The results of the latest reproduction number estimates (based on estimated cases with a date of infection on the 2020-03-24) in Italy, stratified by region, can be summarised by whether cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 cases reported on a single day are not included in the analysis (light grey).

National summary

Summary (estimates as of the 2020-03-24)

Table 1: Latest estimates (as of the 2020-03-24) of the number of cases by date of infection, the expected change in daily cases, the effective reproduction number, the doubling time, and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.
Estimate
New cases by infection date 5340 (3343 – 7666)
Expected change in daily cases Unsure
Effective reproduction no. 1 (0.8 – 1.3)
Doubling time (days) -100 (14 – Inf)
Adjusted R-squared 0.41 (-0.17 – 0.85)

Reported cases, their estimated date of infection, and time-varying reproduction number estimates


Figure 2: A.) Cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates are shown until the 2020-03-24.Dark grey ribbon = 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Time-varying rate of spread and doubling time


Figure 3: A.) Time-varying estimate of the rate of spread, B.) Time-varying estimate of the doubling time in days (note that when the rate of spread is negative the doubling time is assumed to be infinite), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates are shown until the 2020-03-24. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Regional Breakdown

Data availability

Limitations

Summary of latest reproduction number and case count estimates by date of infection


Figure 4: Cases with date of infection on the 2020-03-24 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily cases and shaded based on the expected change in daily cases. The dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.

Reproduction numbers over time in the six regions expected to have the most incident cases


Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-24. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.

Reported cases and their estimated date of infection in the six regions expected to have the most incident cases


Figure 6: Cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-24. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Reproduction numbers over time in all regions


Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-24. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.

Reported cases and their estimated date of infection in all regions

Figure 8: Cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-24. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Latest estimates (as of the 2020-03-24)

Table 2: Latest estimates (as of the 2020-03-24) of the number of cases by date of infection, the effective reproduction number, and the doubling time in each region. The mean and 90% credible interval is shown.
Region New cases by infection date Expected change in daily cases Effective reproduction no. Doubling time (days)
Abruzzo 73 (31 – 105) Unsure 0.9 (0.6 – 1.2) -18 (17 – Inf)
Calabria 31 (9 – 51) Likely decreasing 0.8 (0.5 – 1.1) -10 (14 – Inf)
Campania 215 (103 – 312) Likely increasing 1.3 (0.9 – 1.7) 12 (5.1 – Inf)
Emilia-Romagna 680 (361 – 936) Unsure 0.9 (0.7 – 1.2) -37 (20 – Inf)
Friuli Venezia Giulia 106 (38 – 166) Unsure 1.1 (0.7 – 1.5) 120 (7.8 – Inf)
Lazio 221 (108 – 320) Unsure 1.1 (0.8 – 1.4) 60 (8.7 – Inf)
Liguria 228 (107 – 332) Unsure 1.1 (0.7 – 1.5) 57 (8 – Inf)
Lombardia 1538 (905 – 2227) Likely decreasing 0.8 (0.6 – 1) -19 (45 – Inf)
Marche 168 (81 – 240) Unsure 1 (0.7 – 1.3) 1300 (11 – Inf)
Piemonte 660 (343 – 956) Unsure 1.1 (0.8 – 1.4) 45 (8.8 – Inf)
Puglia 160 (65 – 239) Unsure 1.2 (0.8 – 1.6) 21 (6.2 – Inf)
Sardegna 51 (19 – 85) Unsure 1.1 (0.7 – 1.4) 140 (6.9 – Inf)
Sicilia 103 (50 – 156) Unsure 0.9 (0.6 – 1.2) -25 (17 – Inf)
Toscana 365 (166 – 533) Likely increasing 1.2 (0.8 – 1.5) 23 (6.9 – Inf)
Trentino-Alto Adige 210 (94 – 306) Unsure 1.1 (0.7 – 1.4) 39 (7.6 – Inf)
Umbria 37 (14 – 58) Decreasing 0.7 (0.4 – 1) -7.7 (Inf – Inf)
Valle d’Aosta 35 (7 – 59) Unsure 1.1 (0.7 – 1.5) 35 (4.5 – Inf)
Veneto 534 (275 – 749) Unsure 1.1 (0.8 – 1.4) 92 (10 – Inf)

Abbott, Sam, Joel Hellewell, James D. Munday, and Sebastian Funk. 2020. “NCoVUtils: Utility Functions for the 2019-Ncov Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3635417.

Dipartimento della Protezione Civile. n.d. “Dati Covid-19 Italia.” https://github.com/pcm-dpc/COVID-19.

Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.

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